@inproceedings{wang-etal-2026-tomelp,
    title = "{T}o{MELP}: A Theory-of-Mind Benchmark for Route-Controlled Persuasion under the Elaboration Likelihood Model",
    author = "Wang, Ruirui  and
      Zhang, Haoran  and
      Lan, Tian  and
      Duo, Zehua  and
      Li, Jiang  and
      Gao, Guanglai  and
      Su, Xiangdong",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.findings-acl.1072/",
    pages = "21320--21338",
    ISBN = "979-8-89176-395-1",
    abstract = "Theory of Mind (ToM) is widely regarded as central to effective persuasion, yet existing evaluations often fail to capture the infer{--}apply loop that arises in real-world dialogue. We introduce Theory-of-Mind-Guided Elaboration-Likelihood Persuasion (ToMELP), a benchmark that jointly conditions on the audience persona $p$ and the Elaboration Likelihood Model (ELM) route $r \in \{\text{central}, \text{peripheral}\}$ within persuasive conversations. The benchmark tests whether large language models can perform ToM inference over multi-turn interactions and leverage these inferences for controllable persuasive generation. ToMELP provides a structured interface with evidence annotations, enabling automated evaluation of persuasive effectiveness, route alignment/deviation, evidence quality under the central route, and robustness to perturbations."
}